A Survey on 3D Skeleton Based Person Re-Identification: Taxonomy, Advances, Challenges, and Interdisciplinary Prospects
arXiv:2401.15296v4 Announce Type: replace-cross Abstract: Person re-identification via 3D skeletons is an important emerging research area that attracts increasing attention within the pattern recognition community. With distinctive advantages across various application scenarios, numerous 3D skeleton based person re-identification (SRID) methods with diverse skeleton modeling and learning paradigms have been proposed in recent years. In this paper, we provide a comprehensive review and analysis of recent SRID advances. First of all, we define the SRID task and provide an overview of its origi
The proliferation of 3D sensing technologies and advances in AI, particularly computer vision, are making 3D skeleton-based person re-identification an increasingly viable and relevant research area.
This survey highlights an emerging capability in AI that can enhance security, surveillance, and human-computer interaction, offering more robust and privacy-preserving alternatives to traditional image-based methods.
The focus in person re-identification is shifting from solely 2D visual data to more robust and less privacy-invasive 3D skeletal data, potentially improving accuracy and application scope.
- · AI research institutions
- · Security and surveillance tech providers
- · Robotics developers
- · Legacy 2D visual re-identification systems
- · Companies relying on less accurate tracking methods
Improved person re-identification in challenging environments (e.g., occlusions, varying lighting) due to 3D skeleton data.
Expansion of AI applications in automated monitoring, elderly care, and interactive systems that rely on understanding human movement.
Ethical and regulatory debates around the pervasive tracking and identification of individuals through emergent 3D AI techniques.
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